An Online Model-Following Projection Mechanism Using Reinforcement Learning
An Online Model-Following Projection Mechanism Using Reinforcement Learning
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DOI：
10.1109/tac.2023.3243165
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发表时间：
2023-02
影响因子：
6.8
通讯作者：
M. Abouheaf;Hashim A. Hashim-Hashim-A.-Hashim-36452482;M. Mayyas;K. Vamvoudakis
中科院分区：
文献类型：
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作者：
M. Abouheaf;Hashim A. Hashim-Hashim-A.-Hashim-36452482;M. Mayyas;K. Vamvoudakis
在本文中，我们提出了一个模型跟随控制问题的无模型自适应学习解决方案。该方法采用策略迭代，寻找最优的自适应控制解。它利用了模型跟随误差测量的移动有限视界。此外，利用拉格朗日动力学的投影机制设计了控制策略。它允许实时调整派生的演员-评论家结构，以找到最佳的模型跟随策略并保持优化的适应性能。最后，通过与滑模和高阶无模型自适应控制方法的比较，强调了该框架的有效性。
In this article, we propose a model-free adaptive learning solution for a model-following control problem. This approach employs policy iteration, to find an optimal adaptive control solution. It utilizes a moving finite-horizon of model-following error measurements. In addition, the control strategy is designed by using a projection mechanism that employs Lagrange dynamics. It allows for real-time tuning of derived actor–critic structures to find the optimal model-following strategy and sustain optimized adaptation performance. Finally, the efficacy of the proposed framework is emphasized through a comparison with sliding mode and high-order model-free adaptive control approaches.